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VOL. 1, NO. 12 DECEMBER 2006 EconomicLetter Insights from the FEDERAL RESERVE BANK OF DALLAS Through a Glass, Darkly: How Data Revisions Complicate Monetary Policy by Evan F. Koenig Looking at preliminary Over the course of any year, we receive a veritable tidal wave of data, policymakers numbers on the U.S. economy’s performance—readings on output, inflation, and others may employment, productivity and so much more. Policymakers, business opera- misinterpret what they tors, investors and the general public look to these data to make economic see, leading to mistakes decisions. Unfortunately, some early statistical releases only imperfectly reflect that could harm what’s happening. As more complete and accurate data come out, the view the economy. they provide improves. Looking at preliminary data, policymakers and others may misinterpret what they see, leading to mistakes that could harm the economy. A better understanding of the nature of the revisions that regularly alter the data should lessen the chances of acting on information that doesn’t accurately Chart 1 The Shifting Inflation Picture (12-month PCE inflation, excluding food and energy) B. …and Again in 2005 A. Big Changes in 2003… Percent Percent 3 3 2.5 2.5 July data 2 2 1.5 1.5 June data November data 1 1 December data .5 .5 0 0 ’99 ’00 ’01 ’02 ’03 ’04 ’05 ’99 ’00 ’01 ’02 ’03 ’04 ’05 SOURCE: Bureau of Economic Analysis. A better understanding of the nature of the revisions that regularly alter the data should lessen the chances of acting on information that doesn’t accurately reflect economic realities. reflect economic realities. As an example of the potential importance of data revisions for monetary policy, consider the behavior of personal consumption expenditure (PCE) inflation, excluding food and energy. Core PCE inflation is policymakers’ preferred measure of trend price change because of the PCE price index’s relatively broad coverage and superior tracking of shifts in household spending patterns. As of November 2003, government data showed that core PCE inflation had been held to a fairly narrow 1 to 2 percent range for several years running—a range that several Fed policymakers subsequently identified as their inflation “comfort zone” (Chart 1A). Citing worries about a possible unwelcome fall in inflation, the Federal Open Market Committee had voted to stimulate the economy by cutting the target federal funds rate at its June 2003 meeting. Unfortunately, the broad coverage and shifting spending shares that EconomicLetter 2 FEDERAL RESERVE BANK OF DALLAS make PCE inflation so attractive have a big, practical disadvantage: They make the measure vulnerable to substantial revision. By December 2003, new data had significantly altered the path of core PCE inflation. It was now apparent that inflation had exceeded 2 percent back in 2001 and—of more pressing concern—had been running at 1 percent or below for four months. Cut to June 2005 (Chart 1B). Core PCE inflation appeared to have stabilized at about 1.5 percent. Another month of data, however, brought yet another major revision. Concerns about excessively low inflation in 2003 now seemed possibly exaggerated. Just as important, inflation in much of 2004 and 2005 wasn’t in the middle of the comfort zone after all, but above its 2 percent upper limit. Main Sources of Revisions Most data revisions fall into one of three categories. New estimates of seasonal patterns. Most economic data have a dis- cernible seasonal pattern due to predictable weather and holiday effects. Statistical agencies try to strip out this pattern to make it easier to identify the business-cycle movements of concern to policymakers. But seasonal patterns shift over time and have to be reestimated, which leads to data revisions. Because it generally takes three years of data to estimate seasonal patterns, revisions due to seasonal factors can extend over several years. On the other hand, seasonal patterns shift slowly enough that resulting revisions are usually small. More complete survey responses. Many government data series are based on survey responses. As new responses are processed and old responses are corrected, statisticians are able to improve the accuracy of earlier estimates of what transpired in any particular month. For series updated to capture late-arriving, more complete data, the government typically issues one or two revisions in the months immediately after the initial release. Other revisions follow later, at regular inter- Chart 2 Revisions to March 2005 Texas Job Growth Thousands of jobs 25 21.7 20 15 10.6 10 9.8 5 0 First release, April 2005 First revision, May 2005 Benchmark revision, March 2006 SOURCE: Texas Workforce Commission. vals, as data from annual surveys, censuses or other sources become available. Revisions due to more complete data are responsible for most of the month-to-month and year-to-year changes in economic data. As an example, consider the sequence of official estimates of the number of nonfarm jobs added in Texas during March 2005. The initial estimate, a 10,600-job gain, was released in April 2005 (Chart 2). It was based on survey results for a sample of firms that collectively account for about 40 percent of nonfarm jobs. A first revision to March job growth was released a month later, along with the first estimate of April employment. It reflected corrections to previously received survey responses, as well as late-arriving responses, and showed a slightly smaller job gain. Finally, an annual revision to Texas employment was released in March 2006. It showed an increase twice as large as that previously estimated. Data for each of the other 11 months from October 2004 through September 2005 were revised at the same time. Annual revisions draw on tax reports submitted under Texas unemployment insurance laws. These records capture about 98 percent of nonfarm jobs, and the new estimates are definitive, apart from revisions due to updated seasonal factors. Series derived from surveys with once-and-for-all monthly deadlines aren’t subject to revisions based on new information. They include the unemployment rate, the Conference Board’s Consumer Confidence Index, the Institute for Supply Management’s manufacturing and nonmanufacturing indexes, and the business-conditions indexes compiled by various Federal Reserve Banks. Another example is the Consumer Price Index, which is based on retail prices observed and recorded directly by Labor Department employees. Commodity and financial asset prices, of course, are also not subject to this type of revision. FEDERAL RESERVE BANK OF DALLAS Because it generally takes three years of data to estimate seasonal patterns, revisions due to seasonal factors can extend over several years. 3 EconomicLetter Chart 3 The Case of the Missing Recession (Conference Board Composite Leading Index) Index, December 1998 = 100 130 July data 125 120 115 June data 110 105 100 ’99 ’00 ’01 ’02 ’03 ’04 ’05 ’06 SOURCE: The Conference Board. The revisions in the month or two immediately after the government’s initial releases and revisions due to reestimation of seasonal factors contribute relatively little new information. New methods and definitions, applied retroactively. Finally, revisions occur when new calculation methods or new definitions are applied to old data. Significant revisions of this type are relatively infrequent, and their timing can be irregular. A recent change to the construction of the Conference Board’s Composite Leading Index provides a good example. Looking at the index as it appeared in June 2005, we see that it fell nearly every month between April 2000 and the start of the 2001 recession 11 months later (Chart 3). The cumulative decline was 2 percent. But the index fell by an almost identical amount between May 2004 and May 2005 without a recession. The Conference Board concluded that its leading index was misinterpreting changes in the slope of the yield curve—changes in the difference between long-term and short-term interest rates. In July, the index was reformulated. With one stroke, the 2005 recession warning was eliminated. The seemingly strong record of the leading index is at least in part an illusion due to changes to its construction that have erased its past failures. Assessing and Enhancing Data Taking into account these different types of revisions, just how reliable are early government statistical releases? How close do early releases come to capturing the movements we see in the data available to us today? Let’s start with manufacturing capacity utilization, which is compiled by Federal Reserve Board staff in Washington, D.C. Initial releases capture 87 percent of the variation in today’s capacity utilization data (Table 1). Revisions over the next three months raise the fraction of variation explained only slightly—to 88 percent. After two years of revisions, 6 percent of the movements we observe today remain unexplained. The effects of revisions on real growth as measured by gross domestic product (GDP), industrial produc- EconomicLetter 4 FEDERAL RESERVE BANK OF DALLAS tion and nonfarm jobs are similar. Revisions add little to reliability until a year or more after the initial statistical release. The same holds for inflation as measured by the GDP and PCE price indexes. For the unemployment rate and inflation as measured by the Consumer Price Index, the story is very different. These series are unrevised, except when seasonal factors are updated. Because these updates are small, the initial estimates capture essentially all the information in today’s data. This brief survey suggests that the most important revisions are those undertaken to incorporate new data from surveys and censuses conducted once a year or even less frequently. The revisions in the month or two immediately after the government’s initial releases and revisions due to reestimation of seasonal factors contribute relatively little new information. In addition to government data, useful alternatives or supplements exist that aren’t subject to large revisions. To begin with, formal business and consumer surveys are published by the Institute for Supply Management, various regional Federal Reserve Banks and the Conference Board. If the results of these surveys are revised at all, it’s only from reestimation of seasonal factors. There are also lessstructured surveys, like the roundtables held at the Federal Reserve Bank directors’ meetings and the calls Reserve Bank presidents and their staffs make to business contacts in advance of Federal Open Market Committee meetings. Studies have shown that some of these surveys contain information beyond what’s available from real-time government statistical releases.1 A big advantage of many nongovernmental surveys is their timeliness. The Institute for Supply Management’s manufacturing index, for example, is published the first business day of each month—about two weeks before the Federal Table 1 How Reliable Are Government Data? Economic Indicator Slack Capacity utilization Unemployment rate Growth Real GDP Industrial production Nonfarm jobs Inflation GDP prices PCE prices Consumer Price Index Percentage of today’s variation explained after: First release One quarter One year Two years 87 100 88 100 89 100 94 100 78 82 92 76 80 91 90 82 97 94 86 99 88 94 99 87 94 100 89 89 100 96 93 100 SOURCES: Federal Reserve Board; Bureau of Labor Statistics; Bureau of Economic Analysis; author’s calculations. Reserve’s index of manufacturing output. A drawback is that participants often aren’t selected scientifically and may not be representative of the general population. Moreover, anecdotal accounts, like those in the Fed’s Beige Book, can be difficult for inexperienced readers to interpret. Key market prices can also add to our understanding of the economy. Commodity prices have historically provided early signals of emerging inflation pressures and the strength of the manufacturing sector. Quality and maturity spreads based on financial asset prices provide some of our most reliable indicators of overall real growth prospects. Commodity and financial asset prices have the advantage of being available on a daily basis or even minute by minute. A problem is that although the indicators themselves aren’t subject to revision, their interpretation is. For example, as more manufacturing activity has shifted overseas, the correlation between commodity prices and the strength of the U.S. manufacturing sector has declined.2 Oil price movements were once mostly driven by changes in world supplies. Now, shifts in world demand are increasingly important.3 In some cases, data can be sharpened ahead of official revisions. The Dallas Fed has had great success anticipating revisions to Texas state employment estimates. Recall that in March of each year, job growth estimates through the preceding September are revised using unemployment insurance tax records. These tax records, however, are available quarterly. The Dallas Fed’s Frank Berger takes advantage of this fact to revise our estimates of Texas jobs on an accelerated schedule, using procedures he developed in joint work with colleague Keith Phillips.4 As we’ve seen, the official estimates of March 2005 Texas job growth didn’t reflect the rapid expansion of the state’s economy until a year had passed. The Dallas Fed’s procedures, however, allowed us to anticipate much of the jobs revision (Chart 4). Our superior estimates of past job growth are an important reason why our job growth forecasts consistently outperform those of other analysts. FEDERAL RESERVE BANK OF DALLAS A Recipe for Trouble Seriously misleading conclusions and subpar forecasting results are likely when analysts and policymakers treat heavily revised and first-release data as if they are interchangeable. Let’s look at an example from the realm of inflation forecasting. Lies, damned lies and the markup. British politician Benjamin Disraeli famously remarked that “there are three kinds of lies: lies, damned lies and statistics.” One statistic with great potential to mislead is a measure of profitability called the markup. It equals the dollar value of the goods and services firms produce, less the cost of materials and supplies, all divided by labor compensation. When the markup exceeds 1, firms’ revenues more than cover variable costs. The markup is interesting for several reasons. First, it’s the reciprocal of labor’s share of the value added to production by U.S. firms. When you hear someone say that labor’s share of aggregate output or aggregate income is at a near-record low, that’s equivalent Chart 4 Anticipating Revisions to Texas Job Growth Thousands of jobs 25 21.7 20 17 15 10.6 10 9.8 5 0 First release, April 2005 First revision, May 2005 Dallas Benchmark revision, Fed, March August 2006 2005 SOURCES: Texas Workforce Commission; Federal Reserve Bank of Dallas. 5 EconomicLetter Chart 5 The Markup and Inflation A. Revised Data Seem to Show That the Markup Helps Forecast Inflation… Forecasted – actual inflation (percentage points) 2 1.5 1 .5 0 –.5 –1 1.46 1.48 1.50 1.52 Markup 1.54 1.56 1.58 B. …But the Link Disappears Using Real-Time Markup Data Forecasted – actual inflation (percentage points) 2 1.5 1 .5 0 –.5 –1 1.46 1.48 1.50 1.52 Real-time markup 1.54 1.56 SOURCES: Bureau of Economic Analysis; Blue Chip Economic Indicators; author’s calculations. EconomicLetter 6 FEDERAL RESERVE BANK OF DALLAS 1.58 to the statement that the markup is at a near-record high. In the same vein, when you hear that real wage growth has been lagging behind labor productivity growth, that’s equivalent to the statement that the markup has been rising. Finally—and of greatest importance for monetary policy— whenever the markup is unusually high, theory predicts that competition between firms should gradually drive it back down. That means a high markup should act as a restraining influence on future inflation. Former Fed Chairman Alan Greenspan gave prominent attention to this link in his July 2004 testimony before Congress.5 The markup and inflation. Let’s compare inflation forecast errors in the Blue Chip survey of professional forecasters with the markup at the end of the prior year. We find that from 1984 through 2002, forecasters systematically overpredicted inflation when the markup was high and underpredicted inflation when the markup was low (Chart 5A). Either the Blue Chip forecasters have been ignoring important information, or there’s something not quite right in this relationship. What’s not quite right, of course, is that the markup estimates available to us today aren’t the markup estimates that were available to these forecasters. Sure enough, when we replace today’s markup estimates with the first-release estimates available in real time, the correlation between the markup and inflation disappears (Chart 5B). The markup is useful for understanding inflation after the fact, but no help in predicting it.6 Poor forecasts from confusing current with real-time data. Indeed, the markup is worse than useless for forecasting if you naively assume the relationship between markup estimates and inflation is the same for firstrelease data and subsequent revisions. On its own, the Blue Chip survey successfully anticipates 68 percent of the variation in the next year’s inflation. If you conduct an after-the-fact exercise in which you supplement Blue Chip inflation forecasts with today’s markup data, it appears that you can increase predictive power to 77 percent. However, this exercise is artificial because today’s markup data wouldn’t have been available in real time. Unfortunately, the fact that only first-release data are available for actual forecasting all too often doesn’t stop analysts from using revised data to estimate their forecasting equations. In the case of inflation, if you estimate using revised markup data and then forecast by substituting first-release data as they become available, predictive performance is substantially worse than if you had ignored the markup entirely. Only 57 percent of the variation in next year’s inflation is successfully anticipated. The message is clear: If you’re going to forecast with first-release data, the correct thing to do is to estimate using first-release data.7 Early estimates of the markup nearly worthless. The consequences of confusing revised with first-release data are especially severe in this case because real-time markup data are poor quality. From 1983 to 2002, firstrelease markup estimates accounted for only 5 percent of the variation that we see in today’s markup data. Since 1990, they’ve accounted for only 2 percent. Even with the benefit of a year’s worth of revisions, markup estimates account for just 21 percent of today’s markup variation. So don’t take too seriously claims that labor’s share of output is at a record low or arguments that high profit margins are going to restrain inflation—at least not until the data have been through several annual revisions. Living with Revisions Caution is essential in interpreting early government reports because many data series are subject to large after-the-fact revisions. When reading government statistical releases, it’s best to keep the following in mind: • Seasonal and month-to-month revisions generally have little impact on the information content of government statistical estimates. It’s the less frequent annual, comprehensive and benchmark revisions that really matter. • By supplementing the government’s formal statistical releases with information from other sources, it’s sometimes possible to obtain a more accurate picture of the economy. At the Dallas Fed, we’ve had success using unemployment insurance tax records to make early updates to Texas jobs data. • Revised data showing one variable leading another say next to nothing about whether the first variable is of any practical use in forecasting the second. • Forecasting relationships estimated with heavily revised data are unlikely to perform well when applied to first-release data available in real time. As an example of possible implications for monetary policy, consider inflation targeting. Data revisions potentially affect both how tightly one can realistically expect to control any particular inflation measure and how strongly policy ought to react to early inflation releases. Attempts to target forecasted inflation will benefit if forecasts are as accurate as possible, which requires that heavily revised and early-release data be kept strictly separate. Evan F. Koenig is vice president and senior economist in the Research Department of the Federal Reserve Bank of Dallas. 2 From 1949 through 1996, the correlation between the 12-month growth rates of U.S. manufacturing output and the Journal of Commerce–Economic Cycle Research Institute Industrial Price Index is 0.48. From 1997 to the present, the correlation is only 0.16. 3 “Making Sense of High Oil Prices,” by Stephen P. A. Brown, Federal Reserve Bank of Dallas Southwest Economy, July/August 2006. 4 “Solving the Mystery of the Disappearing January Blip in State Employment Data,” by Franklin D. Berger and Keith R. Phillips, Federal Reserve Bank of Dallas Economic Review, Second Quarter 1994, pp. 53–62; “Reassessing Texas Employment Growth,” by Franklin D. Berger and Keith R. Phillips, Federal Reserve Bank of Dallas Southwest Economy, July/August 1993. 5 Federal Reserve Board’s Semiannual Monetary Policy Report to the Congress, by Alan Greenspan, testimony before the Committee on Banking, Housing and Urban Affairs, U.S. Senate, July 20, 2004. 6 “Is the Markup a Useful Real-Time Predictor of Inflation?” by Evan F. Koenig, Economics Letters, vol. 80, August 2003, pp. 261–67; “The Use and Abuse of Real-Time and Anecdotal Information in Monetary Policymaking,” by Evan F. Koenig, Source OECD: General Economics and Future Studies, vol. 2005, no. 35, pp. 241–53. 7 “The Use and Abuse of ‘Real-Time’ Data in Economic Forecasting,” by Evan F. Koenig, Sheila Dolmas and Jeremy Piger, Review of Economics and Statistics, vol. 85, August 2003, pp. 618–28. Data revisions potentially affect both how tightly one can realistically expect to control any particular inflation measure and how Notes The author thanks Nicole Ball for research assistance. 1 “How Well Does the Beige Book Reflect Economic Activity? Evaluating Qualitative Information Quantitatively,” by Nathan S. Balke and D’Ann Petersen, Journal of Money, Credit and Banking, vol. 34, February 2002, pp. 114–36; “Using the Purchasing Managers’ Index to Assess the Economy’s Strength and the Likely Direction of Monetary Policy,” by Evan F. Koenig, Federal Reserve Bank of Dallas Economic and Financial Policy Review, Issue 6, 2002. FEDERAL RESERVE BANK OF DALLAS strongly policy ought to react to early inflation releases. 7 EconomicLetter EconomicLetter SouthwestEconomy Regional Information You Can Use! 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